Traffic Simulation with Aimsun

  • Jordi CasasEmail author
  • Jaime L. Ferrer
  • David Garcia
  • Josep Perarnau
  • Alex Torday
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 145)


This chapter is dedicated to the Aimsun transport simulation software, with particular emphasis on its dynamic simulation capabilities. The main topics discussed are the modelling of section dynamics using microscopic and mesoscopic approaches, and algorithms for solving the dynamic traffic assignment problem. The introductory section provides background information together with a discussion of the development principles behind Aimsun: integration, modularity, scalability, interoperability, and extensibility. Section 5.1 provides an overview of the project development process covering model building, verification, calibration and validation, and analysis of outputs. Section 5.3 outlines the logic of the microscopic and mesoscopic simulation processes along with information about the behavioural models at each level. Solving the dynamic traffic assignment problem using Aimsun is the focus of Section 5.4. We cover three different methods for tackling the problem, based on dynamic user equilibrium (DUE) and stochastic route choice models with and without memory. In Section 5.5 we turn to the subject of calibration and validation of Aimsun models. This section describes different Aimsun tools which can be used for verification and validation, and provides guidelines or examples relating to the calibration of behavioural models and dynamic traffic assignment algorithms. Section 5.6 looks at the methods that can be used to extend Aimsun’s modelling capabilities. It covers both working with external applications and the use of various programming tools. Α selection of advanced case studies and applications is the focus of Section 5.7. It describes how Aimsun has been used to solve transportation engineering problems with reference to three real-world examples.In the final section, we describe Aimsun Online and discuss its implementation in Madrid as an advanced case study. In the latter part of the section, we comment on some challenges related to such applications and the development needs that such projects give rise to.


Route Choice Traffic Assignment Lane Change Intelligent Transport System Dynamic Traffic Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors and the rest of the staff at TSS – Transport Simulation Systems – would like to express their sincere gratitude to Professor Jaume Barceló at the Technical University of Catalonia (UPC) for his numerous and varied contributions to the inception, design and evolution of Aimsun over the years. This chapter would exist without him.


  1. Aimsun Version 6 User’s Manual (2008) TSS—transport simulation systems, Barcelona, Spain,
  2. Alexiadis V (2007) Role of simulation in corridor management. Presented at TRB2007 simulation workshop, Washington, DC, JanuaryGoogle Scholar
  3. Balci O (1998) Verification, validation and testing. In: Banks J (ed) Handbook of simulation: principles, methodology, advances, applications and practice. John Wiley & Sons-Interscience, New York, USGoogle Scholar
  4. Barceló J, Casas J (2002) Dynamic network simulation with AIMSUN. Presented at the international symposium on transport simulation, Yokohama (also in: Kitamura R, Kuwahara M (eds) Simulation approaches in transportation analysis: recent advances and challenges. Kluwer, 2005)Google Scholar
  5. Barceló J, Casas J (2004a) Heuristic dynamic assignment based on AIMSUN microscopic traffic simulator. Proceedings of TRISTAN V, GuadeloupeGoogle Scholar
  6. Barceló J, Casas J (2004b) Methodological notes on the calibration and validation of microscopic traffic simulation models. Transportation Research Board, 83rd annual meeting, Washington, DC, 2004Google Scholar
  7. Barceló J, Casas J (2006) Stochastic heuristic dynamic assignment based on Aimsun microscopic traffic simulator. Transport Res Rec 1964:70–79CrossRefGoogle Scholar
  8. Barceló, J, Ferrer JL, Grau R (1994) AIMSUN2 and the GETRAM simulation environment. Research report, Departamento de Estadística e Investigación Operativa. Facultad de Informática, Universidad Politécnica de CataluñaGoogle Scholar
  9. Barceló J, Casas J, Ferrer JL, García D (1998a) Modeling advanced transport telematic applications with microscopic simulators: the case of AIMSUN2, simulation technology, science and art. In: Bargiela A, Kerckhoffs E (eds.) Proceedings of the 10th European simulation symposium. Society for Modeling and Computer Simulation International, Vista, California, US, pp 362–367Google Scholar
  10. Barceló J, Ferrer JL, García D, Florian M, Le Saux E (1998b), Parallelization of microscopic traffic simulation for ATT systems analysis. In: Marcotte P. Nguyen S (eds) Equilibrium and advanced transportation modeling. Kluver Acadmic Publishing, Boston/Dordrecht/LondonGoogle Scholar
  11. Barceló J, Casas J, García D, Perarnau J (2006) A hybrid simulation framework for advanced transportation analysis. International symposium on traffic simulation, ISTS 2006, Lausanne, SwitzerlandGoogle Scholar
  12. Ben-Akiva, M, Bierlaire M (1999) Discrete choice methods and their applications to short term travel decisions. In: Hall RW (ed) Handbook of Tranportation science. Kluver Academic Publishers, Boston/Dordrecht/LondonGoogle Scholar
  13. Bleile T, Krautter W, Manstetten D, Schwab T (1996) Traffic simulation at Robert Bosch GmbH. Proceedings of the Euromotor seminar telematic / vehicle and environment, Aachen, Germany, Nov. 11–12Google Scholar
  14. Carey M, Ge YE (2007) Comparison of methods for path flow reassignment for dynamic user equilibrium, May 2007. School of Management & Economics, Queen’s University, Belfast, Northern IrelandGoogle Scholar
  15. Cascetta, E, Nuzzolo A, Russo F, Vitetta A (1996) A modified logit route choice model overcoming path overlapping problems. In: Proceedings of the 13th international symposium on transportation and traffic flow theory. Pergamon Press, Oxford, UKGoogle Scholar
  16. Ferrer JL, Barceló J (1993) AIMSUN2: advanced interactive microscopic simulator for urban and non-urban networks. Research report. Departamento de Estadística e Investigación Operativa, Facultad de Informática, Universidad Politécnica de CataluñaGoogle Scholar
  17. Florian M, Hearn D (1995) Network equilibrium models and algorithms. In: Ball MO et al. (eds) Handbooks in OR and MS,  Chapter 6, vol.8. Elsevier, Amsterdam, Netherlands
  18. Florian M, Mahut M, Tremblay N (2001) A hybrid optimization–mesoscopic simulation dynamic traffic assignment model. Proceedings of the 2001 IEEE intelligent transport systems conference, Oakland, pp 118–123Google Scholar
  19. Florian M, Mahut M, Tremblay N (2002) Application of a simulation-based dynamic traffic assignment model. Presented at the international symposium on transport simulation, Yokohama (also in: Kitamura R, Kuwahara M (eds) Simulation approaches in transportation analysis. Kluwer, 2005, pp 1–21)Google Scholar
  20. Friesz TL, Bernstein D, Smith TE, Tobin RL, Wie BW (1993) A variational inequality formulation of the dynamic network user equilibrium problem. Oper Res 41(1):179–191CrossRefGoogle Scholar
  21. Gipps PG (1981) A behavioural car-following model for computer simulation. Transport Res Board 15-B:105–111CrossRefGoogle Scholar
  22. Gipps PG (1986a) A model for the structure of lane-changing decisions. Transport Res Board 20-B(5):403–414CrossRefGoogle Scholar
  23. Gipps PG (1986b) MULTSIM: a model for simulating vehicular traffic on multi-lane arterial roads. Math Comput Simul 28:291–295CrossRefGoogle Scholar
  24. Janson BN (1991) Dynamic assignment for urban road networks, Transport Res B 25(2/3):143–161CrossRefGoogle Scholar
  25. Kleijnen JPC (1995) Theory and Methodology: Verification and Validation of Simulation Models, European Journal of Operational Research, vol. 82, pp. 145–162. Elsevier, Amsterdam, NetherlandsGoogle Scholar
  26. Law AM, Kelton WD (1991) Simulation modeling and analysis, 2nd edn. McGraw-Hill, New York, USAGoogle Scholar
  27. Liu HX, Ma W, Ban JX, Michardani P (2005) Dynamic equilibrium assignment with microscopic traffic simulation. 8th international IEEE conference on intelligent transport systems, Vienna, AustriaGoogle Scholar
  28. Lo HK, Szeto WY (2002) A cell-based variational inequality formulation of the dynamic user optimal assignment problem. Transport Res B 36:421–443CrossRefGoogle Scholar
  29. Mahmassani H (2001) Dynamic network traffic assignment and simulation methodology for advanced system management applications. Netw Spatial Econ 1:267–292CrossRefGoogle Scholar
  30. Mahut M (1999a) Speed-maximizing car-following models based on safe stopping rules. Transportation Research Board, 78th annual meeting, January 10–14, 1999 Washington DC, USGoogle Scholar
  31. Mahut M (1999b) Behavioural car following models. Report CRT-99-31. Centre for Research on Transportation, University of Montreal, Montreal, CanadaGoogle Scholar
  32. Mahut M (2001) A discrete flow model for dynamic network loading. Ph.D. thesis, Department d'IRO and CRI, University of MontrealGoogle Scholar
  33. Manstetten D, Krautter W, Schwab T (1998) Traffic simulation supporting urban control system development. Proceedings of the 4th world conference on ITS, SeoulGoogle Scholar
  34. Peeta S, Mahmassani H (1995) System optimal and user equilibrium time-dependent traffic assignment in congested networks. Ann Oper Res 60:81–113CrossRefGoogle Scholar
  35. Ran B, Boyce D (1996) Modeling dynamic transportation networks. Springer, Berlin, GermanyGoogle Scholar
  36. Rouphail NM, Sacks J (2003) Thoughts on traffic models calibration and validation. Paper presented at the workshop on traffic modeling, Sitges, SpainGoogle Scholar
  37. Sbayti H, Lu C, Mahmassani H (2007) Efficient implementations of the method of successive averages in simulation-based DTA models for large-scale network applications. TRB 2007 annual meeting, Chicago, Illinois, USGoogle Scholar
  38. Sheffi Y (1985) Urban transportation networks. Equilibrium analysis with mathematical programming methods. Prentice-Hall, Englewood Cliffs, NYGoogle Scholar
  39. Smith MJ (1993) A new dynamic traffic model and the existence and calculation of dynamic user equilibria on congested capacity-constrained road networks. Transport Res Part B 27:49–63CrossRefGoogle Scholar
  40. Stogios Y, Pringle R, Nikolic G (2008) A traffic simulation framework for the greater Toronto area freeway network: concept and challenges. TRB 2008 annual meeting, Washington DC, USGoogle Scholar
  41. Theil H (1966) Applied economic forecasting. North-Holland, Amsterdam, NetherlandsGoogle Scholar
  42. Tong CO, Wong SC (2000) A predictive dynamic traffic assignment model in congested capacity-constrained road networks. Transport Res B 34:625–644CrossRefGoogle Scholar
  43. Torda A, Casas J, Garcia D, Perarnau J (2009) Traffic Simulation as a Central Element for Traffic Management Decision Support Systems. Second International Symposium on Freeway and Tollway Operations (ISFO), Hawai, USAGoogle Scholar
  44. Varia HR, Dhingra SL (2004) Dynamic user equilibrium traffic assignment on congested multidestination network. J Transport Eng 130(2):211–221CrossRefGoogle Scholar
  45. Yoshii T (1999) Standard verification process for traffic simulation model – verification manual. Kochi University of Technology, Kochi, JapanGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jordi Casas
    • 1
    • 2
    Email author
  • Jaime L. Ferrer
    • 1
  • David Garcia
    • 1
  • Josep Perarnau
    • 1
  • Alex Torday
    • 1
  1. 1.TSS – Transport Simulation SystemsBarcelonaSpain
  2. 2.Universitat de VicVicSpain

Personalised recommendations